Overview

Dataset statistics

Number of variables14
Number of observations35753
Missing cells763
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.0 MiB
Average record size in memory441.3 B

Variable types

Categorical6
Numeric8

Alerts

district has a high cardinality: 80 distinct valuesHigh cardinality
site_code has a high cardinality: 155 distinct valuesHigh cardinality
stock_initial is highly overall correlated with stock_distributed and 2 other fieldsHigh correlation
stock_distributed is highly overall correlated with stock_initial and 1 other fieldsHigh correlation
stock_end is highly overall correlated with stock_initial and 1 other fieldsHigh correlation
average_monthly_consumption is highly overall correlated with stock_initial and 2 other fieldsHigh correlation
region is highly overall correlated with district and 1 other fieldsHigh correlation
district is highly overall correlated with region and 1 other fieldsHigh correlation
site_type is highly overall correlated with region and 1 other fieldsHigh correlation
stock_ordered has 763 (2.1%) missing valuesMissing
stock_adjustment is highly skewed (γ1 = 23.37625509)Skewed
stock_ordered is highly skewed (γ1 = 39.96661947)Skewed
stock_initial has 12431 (34.8%) zerosZeros
stock_received has 29360 (82.1%) zerosZeros
stock_distributed has 17738 (49.6%) zerosZeros
stock_adjustment has 34013 (95.1%) zerosZeros
stock_end has 11976 (33.5%) zerosZeros
average_monthly_consumption has 13191 (36.9%) zerosZeros
stock_ordered has 21552 (60.3%) zerosZeros

Reproduction

Analysis started2023-09-02 16:39:59.330774
Analysis finished2023-09-02 16:40:07.922186
Duration8.59 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

year
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
2018
11192 
2017
10356 
2016
8031 
2019
6174 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters143012
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2018 11192
31.3%
2017 10356
29.0%
2016 8031
22.5%
2019 6174
17.3%

Length

2023-09-02T18:40:08.002448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-02T18:40:08.102537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2018 11192
31.3%
2017 10356
29.0%
2016 8031
22.5%
2019 6174
17.3%

Most occurring characters

ValueCountFrequency (%)
2 35753
25.0%
0 35753
25.0%
1 35753
25.0%
8 11192
 
7.8%
7 10356
 
7.2%
6 8031
 
5.6%
9 6174
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 143012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 35753
25.0%
0 35753
25.0%
1 35753
25.0%
8 11192
 
7.8%
7 10356
 
7.2%
6 8031
 
5.6%
9 6174
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Common 143012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 35753
25.0%
0 35753
25.0%
1 35753
25.0%
8 11192
 
7.8%
7 10356
 
7.2%
6 8031
 
5.6%
9 6174
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 143012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 35753
25.0%
0 35753
25.0%
1 35753
25.0%
8 11192
 
7.8%
7 10356
 
7.2%
6 8031
 
5.6%
9 6174
 
4.3%

month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1694124
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size558.6 KiB
2023-09-02T18:40:08.192168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4290791
Coefficient of variation (CV)0.5558194
Kurtosis-1.1732673
Mean6.1694124
Median Absolute Deviation (MAD)3
Skewness0.16188637
Sum220575
Variance11.758583
MonotonicityNot monotonic
2023-09-02T18:40:08.262480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 3459
9.7%
5 3439
9.6%
4 3427
9.6%
3 3355
9.4%
2 3223
9.0%
1 3222
9.0%
12 2695
7.5%
11 2677
7.5%
10 2628
7.4%
9 2586
7.2%
Other values (2) 5042
14.1%
ValueCountFrequency (%)
1 3222
9.0%
2 3223
9.0%
3 3355
9.4%
4 3427
9.6%
5 3439
9.6%
6 3459
9.7%
7 2492
7.0%
8 2550
7.1%
9 2586
7.2%
10 2628
7.4%
ValueCountFrequency (%)
12 2695
7.5%
11 2677
7.5%
10 2628
7.4%
9 2586
7.2%
8 2550
7.1%
7 2492
7.0%
6 3459
9.7%
5 3439
9.6%
4 3427
9.6%
3 3355
9.4%

region
Categorical

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
ABIDJAN 2
6504 
ABIDJAN 1-GRANDS PONTS
4597 
AGNEBY-TIASSA-ME
2389 
PORO-TCHOLOGO-BAGOUE
2319 
GBOKLE-NAWA-SAN PEDRO
2266 
Other values (15)
17678 

Length

Max length23
Median length20
Mean length13.891366
Min length3

Characters and Unicode

Total characters496658
Distinct characters29
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINDENIE-DJUABLIN
2nd rowINDENIE-DJUABLIN
3rd rowINDENIE-DJUABLIN
4th rowINDENIE-DJUABLIN
5th rowINDENIE-DJUABLIN

Common Values

ValueCountFrequency (%)
ABIDJAN 2 6504
18.2%
ABIDJAN 1-GRANDS PONTS 4597
12.9%
AGNEBY-TIASSA-ME 2389
 
6.7%
PORO-TCHOLOGO-BAGOUE 2319
 
6.5%
GBOKLE-NAWA-SAN PEDRO 2266
 
6.3%
N'ZI-IFOU-MORONOU 2132
 
6.0%
BELIER 1625
 
4.5%
BOUNKANI-GONTOUGO 1593
 
4.5%
SUD-COMOE 1491
 
4.2%
HAUT-SASSANDRA 1295
 
3.6%
Other values (10) 9542
26.7%

Length

2023-09-02T18:40:08.342129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
abidjan 11101
20.7%
2 6504
12.1%
1-grands 4597
 
8.6%
ponts 4597
 
8.6%
agneby-tiassa-me 2389
 
4.4%
poro-tchologo-bagoue 2319
 
4.3%
gbokle-nawa-san 2266
 
4.2%
pedro 2266
 
4.2%
n'zi-ifou-moronou 2132
 
4.0%
belier 1625
 
3.0%
Other values (13) 13921
25.9%

Most occurring characters

ValueCountFrequency (%)
A 60152
 
12.1%
O 55720
 
11.2%
N 43342
 
8.7%
- 32822
 
6.6%
B 27613
 
5.6%
I 25728
 
5.2%
D 24835
 
5.0%
G 22926
 
4.6%
E 22869
 
4.6%
S 21614
 
4.4%
Other values (19) 159037
32.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 432639
87.1%
Dash Punctuation 32822
 
6.6%
Space Separator 17964
 
3.6%
Decimal Number 11101
 
2.2%
Other Punctuation 2132
 
0.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 60152
13.9%
O 55720
12.9%
N 43342
10.0%
B 27613
 
6.4%
I 25728
 
5.9%
D 24835
 
5.7%
G 22926
 
5.3%
E 22869
 
5.3%
S 21614
 
5.0%
U 20135
 
4.7%
Other values (14) 107705
24.9%
Decimal Number
ValueCountFrequency (%)
2 6504
58.6%
1 4597
41.4%
Dash Punctuation
ValueCountFrequency (%)
- 32822
100.0%
Space Separator
ValueCountFrequency (%)
17964
100.0%
Other Punctuation
ValueCountFrequency (%)
' 2132
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 432639
87.1%
Common 64019
 
12.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 60152
13.9%
O 55720
12.9%
N 43342
10.0%
B 27613
 
6.4%
I 25728
 
5.9%
D 24835
 
5.7%
G 22926
 
5.3%
E 22869
 
5.3%
S 21614
 
5.0%
U 20135
 
4.7%
Other values (14) 107705
24.9%
Common
ValueCountFrequency (%)
- 32822
51.3%
17964
28.1%
2 6504
 
10.2%
1 4597
 
7.2%
' 2132
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 496658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 60152
 
12.1%
O 55720
 
11.2%
N 43342
 
8.7%
- 32822
 
6.6%
B 27613
 
5.6%
I 25728
 
5.2%
D 24835
 
5.0%
G 22926
 
4.6%
E 22869
 
4.6%
S 21614
 
4.4%
Other values (19) 159037
32.0%

district
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct80
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
KOUMASSI-PORT BOUET-VRIDI
 
2315
COCODY-BINGERVILLE
 
2016
ADJAME-PLATEAU-ATTECOUBE
 
1616
YOPOUGON-OUEST-SONGON
 
1366
ABOBO-EST
 
1298
Other values (75)
27142 

Length

Max length25
Median length19
Mean length10.836629
Min length3

Characters and Unicode

Total characters387442
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowABENGOUROU
2nd rowABENGOUROU
3rd rowABENGOUROU
4th rowABENGOUROU
5th rowABENGOUROU

Common Values

ValueCountFrequency (%)
KOUMASSI-PORT BOUET-VRIDI 2315
 
6.5%
COCODY-BINGERVILLE 2016
 
5.6%
ADJAME-PLATEAU-ATTECOUBE 1616
 
4.5%
YOPOUGON-OUEST-SONGON 1366
 
3.8%
ABOBO-EST 1298
 
3.6%
SOUBRE 1058
 
3.0%
TANDA 937
 
2.6%
TIASSALE 845
 
2.4%
YOPOUGON-EST 800
 
2.2%
GAGNOA 800
 
2.2%
Other values (70) 22702
63.5%

Length

2023-09-02T18:40:08.442376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
koumassi-port 2315
 
6.0%
bouet-vridi 2315
 
6.0%
cocody-bingerville 2016
 
5.2%
adjame-plateau-attecoube 1616
 
4.2%
yopougon-ouest-songon 1366
 
3.5%
abobo-est 1298
 
3.4%
soubre 1058
 
2.7%
tanda 937
 
2.4%
tiassale 845
 
2.2%
gagnoa 800
 
2.1%
Other values (73) 23970
62.2%

Most occurring characters

ValueCountFrequency (%)
O 53186
13.7%
A 41404
 
10.7%
E 30982
 
8.0%
U 28152
 
7.3%
I 24107
 
6.2%
S 20980
 
5.4%
N 20414
 
5.3%
T 18688
 
4.8%
B 17292
 
4.5%
- 16780
 
4.3%
Other values (17) 115457
29.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 367594
94.9%
Dash Punctuation 16780
 
4.3%
Space Separator 2783
 
0.7%
Other Punctuation 285
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 53186
14.5%
A 41404
11.3%
E 30982
 
8.4%
U 28152
 
7.7%
I 24107
 
6.6%
S 20980
 
5.7%
N 20414
 
5.6%
T 18688
 
5.1%
B 17292
 
4.7%
R 15822
 
4.3%
Other values (14) 96567
26.3%
Dash Punctuation
ValueCountFrequency (%)
- 16780
100.0%
Space Separator
ValueCountFrequency (%)
2783
100.0%
Other Punctuation
ValueCountFrequency (%)
' 285
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 367594
94.9%
Common 19848
 
5.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 53186
14.5%
A 41404
11.3%
E 30982
 
8.4%
U 28152
 
7.7%
I 24107
 
6.6%
S 20980
 
5.7%
N 20414
 
5.6%
T 18688
 
5.1%
B 17292
 
4.7%
R 15822
 
4.3%
Other values (14) 96567
26.3%
Common
ValueCountFrequency (%)
- 16780
84.5%
2783
 
14.0%
' 285
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 53186
13.7%
A 41404
 
10.7%
E 30982
 
8.0%
U 28152
 
7.3%
I 24107
 
6.2%
S 20980
 
5.4%
N 20414
 
5.3%
T 18688
 
4.8%
B 17292
 
4.5%
- 16780
 
4.3%
Other values (17) 115457
29.8%

site_code
Categorical

Distinct155
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
C1015
 
416
C1087
 
413
C1030
 
405
C1055
 
404
C1112
 
398
Other values (150)
33717 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters178765
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC4001
2nd rowC4001
3rd rowC4001
4th rowC4001
5th rowC4001

Common Values

ValueCountFrequency (%)
C1015 416
 
1.2%
C1087 413
 
1.2%
C1030 405
 
1.1%
C1055 404
 
1.1%
C1112 398
 
1.1%
C1077 392
 
1.1%
C1090 385
 
1.1%
C5063 381
 
1.1%
C2011 373
 
1.0%
C1027 366
 
1.0%
Other values (145) 31820
89.0%

Length

2023-09-02T18:40:08.522271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c1015 416
 
1.2%
c1087 413
 
1.2%
c1030 405
 
1.1%
c1055 404
 
1.1%
c1112 398
 
1.1%
c1077 392
 
1.1%
c1090 385
 
1.1%
c5063 381
 
1.1%
c2011 373
 
1.0%
c1027 366
 
1.0%
Other values (145) 31820
89.0%

Most occurring characters

ValueCountFrequency (%)
0 41361
23.1%
C 35753
20.0%
1 29393
16.4%
2 18278
10.2%
5 10153
 
5.7%
4 9087
 
5.1%
6 9066
 
5.1%
3 8344
 
4.7%
7 6348
 
3.6%
8 5750
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 143012
80.0%
Uppercase Letter 35753
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 41361
28.9%
1 29393
20.6%
2 18278
12.8%
5 10153
 
7.1%
4 9087
 
6.4%
6 9066
 
6.3%
3 8344
 
5.8%
7 6348
 
4.4%
8 5750
 
4.0%
9 5232
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
C 35753
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 143012
80.0%
Latin 35753
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 41361
28.9%
1 29393
20.6%
2 18278
12.8%
5 10153
 
7.1%
4 9087
 
6.4%
6 9066
 
6.3%
3 8344
 
5.8%
7 6348
 
4.4%
8 5750
 
4.0%
9 5232
 
3.7%
Latin
ValueCountFrequency (%)
C 35753
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 178765
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 41361
23.1%
C 35753
20.0%
1 29393
16.4%
2 18278
10.2%
5 10153
 
5.7%
4 9087
 
5.1%
6 9066
 
5.1%
3 8344
 
4.7%
7 6348
 
3.6%
8 5750
 
3.2%

product_code
Categorical

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
AS27133
5368 
AS27000
5259 
AS27134
4708 
AS27137
4449 
AS27132
4436 
Other values (6)
11533 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters250271
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAS27134
2nd rowAS27132
3rd rowAS27000
4th rowAS27137
5th rowAS27138

Common Values

ValueCountFrequency (%)
AS27133 5368
15.0%
AS27000 5259
14.7%
AS27134 4708
13.2%
AS27137 4449
12.4%
AS27132 4436
12.4%
AS27138 4060
11.4%
AS27139 2347
6.6%
AS46000 1981
 
5.5%
AS42018 1550
 
4.3%
AS17005 1248
 
3.5%

Length

2023-09-02T18:40:08.602243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
as27133 5368
15.0%
as27000 5259
14.7%
as27134 4708
13.2%
as27137 4449
12.4%
as27132 4436
12.4%
as27138 4060
11.4%
as27139 2347
6.6%
as46000 1981
 
5.5%
as42018 1550
 
4.3%
as17005 1248
 
3.5%

Most occurring characters

ValueCountFrequency (%)
2 37307
14.9%
7 36324
14.5%
A 35753
14.3%
S 35753
14.3%
3 30736
12.3%
1 28860
11.5%
0 25766
10.3%
4 8239
 
3.3%
8 5610
 
2.2%
9 2347
 
0.9%
Other values (2) 3576
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 178765
71.4%
Uppercase Letter 71506
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 37307
20.9%
7 36324
20.3%
3 30736
17.2%
1 28860
16.1%
0 25766
14.4%
4 8239
 
4.6%
8 5610
 
3.1%
9 2347
 
1.3%
6 2328
 
1.3%
5 1248
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
A 35753
50.0%
S 35753
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 178765
71.4%
Latin 71506
 
28.6%

Most frequent character per script

Common
ValueCountFrequency (%)
2 37307
20.9%
7 36324
20.3%
3 30736
17.2%
1 28860
16.1%
0 25766
14.4%
4 8239
 
4.6%
8 5610
 
3.1%
9 2347
 
1.3%
6 2328
 
1.3%
5 1248
 
0.7%
Latin
ValueCountFrequency (%)
A 35753
50.0%
S 35753
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 250271
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 37307
14.9%
7 36324
14.5%
A 35753
14.3%
S 35753
14.3%
3 30736
12.3%
1 28860
11.5%
0 25766
10.3%
4 8239
 
3.3%
8 5610
 
2.2%
9 2347
 
0.9%
Other values (2) 3576
 
1.4%

stock_initial
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct798
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.245518
Minimum0
Maximum4320
Zeros12431
Zeros (%)34.8%
Negative0
Negative (%)0.0%
Memory size558.6 KiB
2023-09-02T18:40:08.692487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12
Q369
95-th percentile252
Maximum4320
Range4320
Interquartile range (IQR)69

Descriptive statistics

Standard deviation168.66154
Coefficient of variation (CV)2.6667746
Kurtosis140.02872
Mean63.245518
Median Absolute Deviation (MAD)12
Skewness9.4428505
Sum2261217
Variance28446.715
MonotonicityNot monotonic
2023-09-02T18:40:08.792289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12431
34.8%
10 957
 
2.7%
1 829
 
2.3%
100 696
 
1.9%
2 648
 
1.8%
20 615
 
1.7%
30 546
 
1.5%
50 514
 
1.4%
3 489
 
1.4%
5 463
 
1.3%
Other values (788) 17565
49.1%
ValueCountFrequency (%)
0 12431
34.8%
1 829
 
2.3%
2 648
 
1.8%
3 489
 
1.4%
4 381
 
1.1%
5 463
 
1.3%
6 327
 
0.9%
7 325
 
0.9%
8 364
 
1.0%
9 387
 
1.1%
ValueCountFrequency (%)
4320 5
 
< 0.1%
3428 1
 
< 0.1%
3061 1
 
< 0.1%
3039 1
 
< 0.1%
3024 1
 
< 0.1%
3015 1
 
< 0.1%
2709 1
 
< 0.1%
2691 2
 
< 0.1%
2632 1
 
< 0.1%
2592 21
0.1%

stock_received
Real number (ℝ)

Distinct259
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.846055
Minimum0
Maximum3534
Zeros29360
Zeros (%)82.1%
Negative0
Negative (%)0.0%
Memory size558.6 KiB
2023-09-02T18:40:08.895467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile100
Maximum3534
Range3534
Interquartile range (IQR)0

Descriptive statistics

Standard deviation70.631782
Coefficient of variation (CV)4.7576129
Kurtosis451.68429
Mean14.846055
Median Absolute Deviation (MAD)0
Skewness15.620155
Sum530791
Variance4988.8487
MonotonicityNot monotonic
2023-09-02T18:40:08.999248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29360
82.1%
100 962
 
2.7%
10 728
 
2.0%
50 557
 
1.6%
20 484
 
1.4%
30 384
 
1.1%
25 269
 
0.8%
200 250
 
0.7%
1 170
 
0.5%
5 158
 
0.4%
Other values (249) 2431
 
6.8%
ValueCountFrequency (%)
0 29360
82.1%
1 170
 
0.5%
2 128
 
0.4%
3 97
 
0.3%
4 74
 
0.2%
5 158
 
0.4%
6 52
 
0.1%
7 56
 
0.2%
8 16
 
< 0.1%
9 42
 
0.1%
ValueCountFrequency (%)
3534 1
< 0.1%
3024 1
< 0.1%
2500 1
< 0.1%
2160 1
< 0.1%
1824 1
< 0.1%
1800 2
< 0.1%
1623 1
< 0.1%
1550 1
< 0.1%
1536 1
< 0.1%
1500 1
< 0.1%

stock_distributed
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct338
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.764327
Minimum0
Maximum1728
Zeros17738
Zeros (%)49.6%
Negative0
Negative (%)0.0%
Memory size558.6 KiB
2023-09-02T18:40:09.102194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q313
95-th percentile71
Maximum1728
Range1728
Interquartile range (IQR)13

Descriptive statistics

Standard deviation39.848242
Coefficient of variation (CV)2.6989541
Kurtosis214.36183
Mean14.764327
Median Absolute Deviation (MAD)1
Skewness9.8685132
Sum527869
Variance1587.8824
MonotonicityNot monotonic
2023-09-02T18:40:09.192173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17738
49.6%
1 1388
 
3.9%
2 1088
 
3.0%
10 1023
 
2.9%
3 976
 
2.7%
5 820
 
2.3%
4 693
 
1.9%
6 657
 
1.8%
7 538
 
1.5%
20 505
 
1.4%
Other values (328) 10327
28.9%
ValueCountFrequency (%)
0 17738
49.6%
1 1388
 
3.9%
2 1088
 
3.0%
3 976
 
2.7%
4 693
 
1.9%
5 820
 
2.3%
6 657
 
1.8%
7 538
 
1.5%
8 461
 
1.3%
9 485
 
1.4%
ValueCountFrequency (%)
1728 1
< 0.1%
1183 1
< 0.1%
1085 1
< 0.1%
1008 1
< 0.1%
1000 1
< 0.1%
960 1
< 0.1%
880 1
< 0.1%
864 1
< 0.1%
800 1
< 0.1%
720 1
< 0.1%

stock_adjustment
Real number (ℝ)

SKEWED  ZEROS 

Distinct389
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.96115011
Minimum-1440
Maximum3003
Zeros34013
Zeros (%)95.1%
Negative585
Negative (%)1.6%
Memory size558.6 KiB
2023-09-02T18:40:09.302128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1440
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum3003
Range4443
Interquartile range (IQR)0

Descriptive statistics

Standard deviation37.883099
Coefficient of variation (CV)39.414342
Kurtosis2008.8713
Mean0.96115011
Median Absolute Deviation (MAD)0
Skewness23.376255
Sum34364
Variance1435.1292
MonotonicityNot monotonic
2023-09-02T18:40:09.394279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 34013
95.1%
10 87
 
0.2%
1 62
 
0.2%
2 51
 
0.1%
20 51
 
0.1%
-1 49
 
0.1%
25 43
 
0.1%
100 42
 
0.1%
50 41
 
0.1%
3 40
 
0.1%
Other values (379) 1274
 
3.6%
ValueCountFrequency (%)
-1440 2
< 0.1%
-817 1
< 0.1%
-747 1
< 0.1%
-720 1
< 0.1%
-696 1
< 0.1%
-661 1
< 0.1%
-600 1
< 0.1%
-588 1
< 0.1%
-576 1
< 0.1%
-510 1
< 0.1%
ValueCountFrequency (%)
3003 1
< 0.1%
2574 1
< 0.1%
1440 1
< 0.1%
1287 1
< 0.1%
1085 1
< 0.1%
894 1
< 0.1%
852 1
< 0.1%
818 1
< 0.1%
673 1
< 0.1%
600 1
< 0.1%

stock_end
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct804
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.288395
Minimum0
Maximum4320
Zeros11976
Zeros (%)33.5%
Negative0
Negative (%)0.0%
Memory size558.6 KiB
2023-09-02T18:40:09.502114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median13
Q370
95-th percentile254
Maximum4320
Range4320
Interquartile range (IQR)70

Descriptive statistics

Standard deviation170.84848
Coefficient of variation (CV)2.6575322
Kurtosis137.74798
Mean64.288395
Median Absolute Deviation (MAD)13
Skewness9.4180673
Sum2298503
Variance29189.203
MonotonicityNot monotonic
2023-09-02T18:40:09.593128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11976
33.5%
10 977
 
2.7%
1 833
 
2.3%
100 694
 
1.9%
2 652
 
1.8%
20 626
 
1.8%
30 552
 
1.5%
50 526
 
1.5%
3 500
 
1.4%
5 486
 
1.4%
Other values (794) 17931
50.2%
ValueCountFrequency (%)
0 11976
33.5%
1 833
 
2.3%
2 652
 
1.8%
3 500
 
1.4%
4 388
 
1.1%
5 486
 
1.4%
6 335
 
0.9%
7 337
 
0.9%
8 372
 
1.0%
9 396
 
1.1%
ValueCountFrequency (%)
4320 5
< 0.1%
3428 1
 
< 0.1%
3061 1
 
< 0.1%
3039 1
 
< 0.1%
3024 1
 
< 0.1%
3015 1
 
< 0.1%
3000 1
 
< 0.1%
2709 1
 
< 0.1%
2691 2
 
< 0.1%
2632 1
 
< 0.1%

average_monthly_consumption
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct313
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.606439
Minimum0
Maximum864
Zeros13191
Zeros (%)36.9%
Negative0
Negative (%)0.0%
Memory size558.6 KiB
2023-09-02T18:40:09.702074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q316
95-th percentile65
Maximum864
Range864
Interquartile range (IQR)16

Descriptive statistics

Standard deviation32.521384
Coefficient of variation (CV)2.2265101
Kurtosis68.435678
Mean14.606439
Median Absolute Deviation (MAD)3
Skewness6.2185845
Sum522224
Variance1057.6404
MonotonicityNot monotonic
2023-09-02T18:40:09.792082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13191
36.9%
1 1999
 
5.6%
3 1767
 
4.9%
2 1567
 
4.4%
7 1078
 
3.0%
4 1003
 
2.8%
8 928
 
2.6%
5 903
 
2.5%
10 761
 
2.1%
6 749
 
2.1%
Other values (303) 11807
33.0%
ValueCountFrequency (%)
0 13191
36.9%
1 1999
 
5.6%
2 1567
 
4.4%
3 1767
 
4.9%
4 1003
 
2.8%
5 903
 
2.5%
6 749
 
2.1%
7 1078
 
3.0%
8 928
 
2.6%
9 602
 
1.7%
ValueCountFrequency (%)
864 1
< 0.1%
816 1
< 0.1%
624 1
< 0.1%
600 1
< 0.1%
567 2
< 0.1%
496 1
< 0.1%
467 1
< 0.1%
456 1
< 0.1%
432 1
< 0.1%
400 2
< 0.1%

stock_ordered
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct333
Distinct (%)1.0%
Missing763
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean26.658102
Minimum0
Maximum10240
Zeros21552
Zeros (%)60.3%
Negative0
Negative (%)0.0%
Memory size558.6 KiB
2023-09-02T18:40:09.893924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q320
95-th percentile120
Maximum10240
Range10240
Interquartile range (IQR)20

Descriptive statistics

Standard deviation107.16608
Coefficient of variation (CV)4.0200192
Kurtosis2976.5893
Mean26.658102
Median Absolute Deviation (MAD)0
Skewness39.966619
Sum932767
Variance11484.569
MonotonicityNot monotonic
2023-09-02T18:40:10.012479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21552
60.3%
100 1765
 
4.9%
10 1729
 
4.8%
50 1319
 
3.7%
20 1139
 
3.2%
30 869
 
2.4%
200 498
 
1.4%
5 437
 
1.2%
1 421
 
1.2%
25 404
 
1.1%
Other values (323) 4857
 
13.6%
(Missing) 763
 
2.1%
ValueCountFrequency (%)
0 21552
60.3%
1 421
 
1.2%
2 322
 
0.9%
3 259
 
0.7%
4 159
 
0.4%
5 437
 
1.2%
6 178
 
0.5%
7 49
 
0.1%
8 82
 
0.2%
9 79
 
0.2%
ValueCountFrequency (%)
10240 1
 
< 0.1%
6000 1
 
< 0.1%
5000 2
 
< 0.1%
3000 2
 
< 0.1%
2525 1
 
< 0.1%
2448 1
 
< 0.1%
2000 5
< 0.1%
1600 2
 
< 0.1%
1550 1
 
< 0.1%
1500 2
 
< 0.1%

site_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Hospital
24779 
Health Center
10332 
University Hospital/National Institute
 
642

Length

Max length38
Median length8
Mean length9.9836098
Min length8

Characters and Unicode

Total characters356944
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHospital
2nd rowHospital
3rd rowHospital
4th rowHospital
5th rowHospital

Common Values

ValueCountFrequency (%)
Hospital 24779
69.3%
Health Center 10332
28.9%
University Hospital/National Institute 642
 
1.8%

Length

2023-09-02T18:40:10.092035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-02T18:40:10.182224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
hospital 24779
52.3%
health 10332
21.8%
center 10332
21.8%
university 642
 
1.4%
hospital/national 642
 
1.4%
institute 642
 
1.4%

Most occurring characters

ValueCountFrequency (%)
t 49295
13.8%
a 37037
10.4%
l 36395
10.2%
H 35753
10.0%
e 32280
9.0%
i 27989
7.8%
s 26705
7.5%
o 26063
7.3%
p 25421
7.1%
n 12258
 
3.4%
Other values (11) 47748
13.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 296675
83.1%
Uppercase Letter 48011
 
13.5%
Space Separator 11616
 
3.3%
Other Punctuation 642
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 49295
16.6%
a 37037
12.5%
l 36395
12.3%
e 32280
10.9%
i 27989
9.4%
s 26705
9.0%
o 26063
8.8%
p 25421
8.6%
n 12258
 
4.1%
r 10974
 
3.7%
Other values (4) 12258
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
H 35753
74.5%
C 10332
 
21.5%
U 642
 
1.3%
N 642
 
1.3%
I 642
 
1.3%
Space Separator
ValueCountFrequency (%)
11616
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 344686
96.6%
Common 12258
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 49295
14.3%
a 37037
10.7%
l 36395
10.6%
H 35753
10.4%
e 32280
9.4%
i 27989
8.1%
s 26705
7.7%
o 26063
7.6%
p 25421
7.4%
n 12258
 
3.6%
Other values (9) 35490
10.3%
Common
ValueCountFrequency (%)
11616
94.8%
/ 642
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 356944
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 49295
13.8%
a 37037
10.4%
l 36395
10.2%
H 35753
10.0%
e 32280
9.0%
i 27989
7.8%
s 26705
7.5%
o 26063
7.3%
p 25421
7.1%
n 12258
 
3.4%
Other values (11) 47748
13.4%

Interactions

2023-09-02T18:40:06.633655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:00.875867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:01.656126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:02.880604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:03.662521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:04.440549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:05.167916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:05.888541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:06.727172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:01.006531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:01.744436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:02.982167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:03.764540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:04.526960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:05.252254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:05.979751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:06.813345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:01.100635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:01.832348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:03.087318image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:03.857555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:04.614053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:05.344548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:06.069170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:06.912464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:01.198712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:01.925516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:03.194337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:03.958189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:04.712150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:05.440979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:06.166613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:07.010390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:01.299125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:02.022371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:03.296648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:04.060534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:04.808365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:05.535973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:06.260716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:07.096971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:01.382337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:02.110898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:03.389029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:04.152384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:04.897068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:05.623224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:06.342226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:07.187038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:01.476759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:02.195357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:03.480606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:04.247195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:04.982169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:05.712042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:06.441034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:07.274788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:01.565078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:02.286411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:03.576541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:04.342063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:05.077943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:05.797570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-02T18:40:06.545285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-09-02T18:40:10.252241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
monthstock_initialstock_receivedstock_distributedstock_adjustmentstock_endaverage_monthly_consumptionstock_orderedyearregiondistrictproduct_codesite_type
month1.0000.0380.0290.014-0.0230.0360.008-0.0320.2340.0000.0000.0300.011
stock_initial0.0381.000-0.0420.586-0.0830.8080.579-0.1030.0210.0530.1200.0690.026
stock_received0.029-0.0421.0000.282-0.0010.3000.2940.0570.0060.0130.0250.0260.000
stock_distributed0.0140.5860.2821.0000.0560.4900.8090.2480.0160.0340.0800.0440.000
stock_adjustment-0.023-0.083-0.0010.0561.0000.0860.052-0.0060.0190.0090.0300.0220.023
stock_end0.0360.8080.3000.4900.0861.0000.527-0.2350.0200.0530.1200.0700.027
average_monthly_consumption0.0080.5790.2940.8090.0520.5271.0000.2740.0180.0530.1220.0680.009
stock_ordered-0.032-0.1030.0570.248-0.006-0.2350.2741.0000.0110.0290.0650.0200.000
year0.2340.0210.0060.0160.0190.0200.0180.0111.0000.0550.0990.1220.055
region0.0000.0530.0130.0340.0090.0530.0530.0290.0551.0000.9990.0710.560
district0.0000.1200.0250.0800.0300.1200.1220.0650.0990.9991.0000.1300.735
product_code0.0300.0690.0260.0440.0220.0700.0680.0200.1220.0710.1301.0000.051
site_type0.0110.0260.0000.0000.0230.0270.0090.0000.0550.5600.7350.0511.000

Missing values

2023-09-02T18:40:07.432069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-02T18:40:07.772467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

yearmonthregiondistrictsite_codeproduct_codestock_initialstock_receivedstock_distributedstock_adjustmentstock_endaverage_monthly_consumptionstock_orderedsite_type
020191INDENIE-DJUABLINABENGOUROUC4001AS2713475021-54018100.0Hospital
120191INDENIE-DJUABLINABENGOUROUC4001AS2713230300210.0Hospital
220191INDENIE-DJUABLINABENGOUROUC4001AS270000752205390.0Hospital
320191INDENIE-DJUABLINABENGOUROUC4001AS271372000200.0Hospital
420191INDENIE-DJUABLINABENGOUROUC4001AS27138450204310.0Hospital
520191INDENIE-DJUABLINABENGOUROUC4001AS2713350501908160.0Hospital
620191INDENIE-DJUABLINABENGOUROUC4023AS270004000040100.0Health Center
720191INDENIE-DJUABLINABENGOUROUC4023AS271331500290121510.0Health Center
820192INDENIE-DJUABLINABENGOUROUC4001AS271340000018100.0Hospital
920192INDENIE-DJUABLINABENGOUROUC4001AS2713200000210.0Hospital
yearmonthregiondistrictsite_codeproduct_codestock_initialstock_receivedstock_distributedstock_adjustmentstock_endaverage_monthly_consumptionstock_orderedsite_type
35743201610LOH-DJIBOUAGUITRYC2055AS271340100909130.0Hospital
35744201610LOH-DJIBOUAGUITRYC2055AS17005460004600.0Hospital
3574520167LOH-DJIBOUAGUITRYC2055AS2713713050830.0Hospital
3574620167LOH-DJIBOUAGUITRYC2055AS27139000000100.0Hospital
3574720167LOH-DJIBOUAGUITRYC2055AS270001810150131610.0Hospital
3574820167LOH-DJIBOUAGUITRYC2055AS2713305025025825.0Hospital
3574920167LOH-DJIBOUAGUITRYC2055AS2713815000015000.0Hospital
3575020167LOH-DJIBOUAGUITRYC2055AS27132110407130.0Hospital
3575120167LOH-DJIBOUAGUITRYC2055AS2713480800100.0Hospital
3575220167LOH-DJIBOUAGUITRYC2055AS17005480204610.0Hospital